Adaptive Multiagent AI-Driven Waste Management

The global waste generation continues to increase at a rapid rate due to the growing population and is predicted to reach 3.4 billion tons annually by 2050. Traditional waste collection systems, which heavily depend on fixed schedule collections, can lead to inefficiencies, high operational costs and overflowing of bins. Sensor-equipped bins have led waste management to be smart with the help of recent advancements, but they still deal with inaccuracies in sensor data collection. The information collected from the ultrasonic and weight sensors contains noise, which makes it difficult to make decisions about fill-level detection. This study proposes a solid framework that integrates filtering techniques and advanced signal processing to reduce noise in data collection for smart waste systems. The architecture combines IoT-enabled bins, data collection, and machine learning models with a hybrid filtering pipeline to provide reliable, real-time insights. The experiments on the three different datasets, that the filtered noise data can increase the classification accuracy from 65%-66% with raw sensor data to over 91%-93% with the filtered signals. These results, consistently obtained across all datasets, confirm that filtering the data enhances the quality and reliability of the sensor data in the waste management system and provides correct waste bin collection status. Such improvement not just strengthens the data results but also builds trust among societies across this rapid-growing urbanization.